Spaceborne Synthetic Aperture Radar Data Research Articles

Retrieval of historical surface displacements of the Baige landslide from time-series SAR observations for retrospective analysis of the collapse event

Landslides and resultant barrier lakes are significant threats to human lives and infrastructures. Three-dimensional (3D) surface displacements can give vital clues to the exploration of internal structure of landslides, but they are difficult to be retrieved from spaceborne Synthetic Aperture Radar (SAR) observations due to the intrinsic limitation of SAR imaging geometry. Meanwhile, studies on predicting slope failure based on SAR-measured displacements are rarely seen. Here, we used SAR pixel offset tracking to investigate the Baige landslide before the collapse on 10 October 2018. 3D surface displacements retrieved by combining satellite SAR and optical observations revealed heterogeneous spatial patterns within the landslide complex. We observed linear secondary creep and accelerating tertiary creep prior to the failure from multi-sensor SAR data. The possibility of forecasting the failure was demonstrated by applying an inverse velocity method to the time-series displacements measured by Sentinel-1 during the tertiary creep, which is valuable for risk evaluation and disaster early warning.

Processing of Very High Resolution GF-3 SAR Spotlight Data With Non-Start–Stop Model and Correction of Curved Orbit

A novel imaging algorithm is presented in this article for focusing the very high resolution spaceborne synthetic aperture radar (SAR) data of spotlight mode. The long synthetic aperture time in spotlight mode brings new problems, such as Doppler spectrum aliasing and curved orbit. The imaging results will be filled with ambiguity and would suffer from resolution reduction if Doppler spectrum aliasing is not handled. The error introduced by curved orbit will degrade resolution of the target and introduces asymmetric sidelobes in azimuth direction. The start–stop approximation is no longer applicable, since it introduces two effect, e.g., “fast-time” effect and “slow-time” effect, which will cause defocusing and range-dependent azimuth shift, respectively if not handled. The proposed algorithm combines the two-steps processing approach (TSPA) and the backprojection algorithm (BPA). First, the initial step of TSPA is used to get a high azimuth sampling rate which is higher than pulse repetition frequency to avoid the Doppler spectrum aliasing. Then, the “fast-time” effect of start–stop approximation is corrected in the 2-D frequency domain. Finally, the BPA is used to correct the error introduced by the curved orbit and the “slow-time” effect of start–stop approximation. The experimental results of spaceborne SAR data acquired by Gaofen-3 (GF-3) SAR system demonstrate the feasibility of the proposed algorithm.

Enhancement of SAR Speckle Denoising Using the Improved Iterative Filter

The recent advancement in synthetic aperture radar (SAR) technology has enabled high-resolution imaging capability that calls for efficient speckle filtering algorithms to preprocess radar imagery. Since the introduction of the Lee sigma filter in 1980, the various versions of the minimum mean square error (MMSE) filter were developed, focusing essentially on how to estimate the processed pixels. For instance, the iterative MMSE (IMMSE) filter that is commonly initialized by the boxcar filter maintains the initially filtered homogeneous areas and corrects the initially blurred spatial details after a few iterations. In this article, an effort is made to enhance the performance of the IMMSE filter in terms of speckle reduction and spatial detail preservation by refining the choice of the initial filter, optimizing its parameters, and improving the estimation of local statistics. Compared with the basic version, results showed that the improved iterative filter considerably enhanced the filtering criteria. When the improved iterative filtering process was initialized by the nonlocal mean filter, for few iterations, the filtering performances were improved. Simulated, airborne (ESAR, Oberpfaffenhofen Germany) and spaceborne (Sentinel 1, Palm Jumeirah Dubai UAE) SAR data were used to assess the filtering performances of the studied filters.

Investigating rapid deforestation and carbon dioxide release in Bangladesh using geospatial information from remote sensing data

Rapid deforestation over the last few years due to the massive influx of refugees from neighboring Myanmar has been reported and is seen as a precursor to environmental disaster, raising the need for more effective monitoring of forest areas. The availability of data from several space-borne synthetic aperture radar (SAR) missions allow enhanced monitoring of forest areas. The objective of this study was to map deforestation in two selected areas located in northeast and southeast Bangladesh using Sentinel-1 imageries and determine the applicability of SAR in forest monitoring in Bangladesh. Towards these purpose satellite imageries from 2017 and 2018 collected by Sentinel-1A and Sentinel-1 Band SAR data in dual-polarization mode were used. In the northeastern area of interest, temporary deforestation was detected, which had occurred in low lying areas due to prolonged flooding. The second area of interest, in the southeast, revealed man-made deforestation in high land areas on an immense scale due to the influx and settlement of seven hundred thousand refugees. The results of the two sub-studies demonstrate the applicability and need of SAR data to effectively monitor deforestation in Bangladesh especially as it allows isolating natural and anthropogenic deforestation.

Local katabatic winds of the Russian Federation and their observation from space using SAR imagery.

Data from the spaceborne synthetic aperture radars (SAR) provide new opportunities for observing and studying local katabatic winds (bora, foehn) in various marine regions of the Russian Federation and adjacent countries. The impact of these winds on the characteristics of small-scale wind waves leads to the formation of characteristic manifestations on the sea surface which are displayed on SAR images. Satellite SAR images, in contrast to sub-satellite observations, make it possible to see the phenomenon in the coastal zones of seas and large lakes, judge the spatial scale of the phenomenon and determine a number of it quantitative characteristics. Analysis of collected SAR images shows that local winds, bora-like and foehn-like, have similar characteristics and cover vast areas of the inland and marginal seas of the Russian Federation. The similarity of the surface manifestations of the local winds, clearly speaks about the same physical and aerohydrodynamic mechanisms of their display on SAR images. It is shown that spaceborne SAR data and images, which are two-dimensional spatial patterns of frozen wind, are extremely useful for research and monitoring local winds over the seas of the Russian Federation, especially where the network of weather stations is extremely rare.

Classification of the global Sentinel-1 SAR vignettes for ocean surface process studies

Spaceborne synthetic aperture radar (SAR) can provide finely-resolved (meters-scale) images of ocean surface roughness day-or-night in nearly all weather conditions. This makes it a unique asset for many geophysical applications. Initially designed for the measurement of directional ocean wave spectra, Sentinel-1 SAR wave mode (WV) vignettes are small 20 km scenes that have been collected globally since 2014. Recent WV data exploration reveals that many important oceanic and atmospheric phenomena are also well captured, but not yet employed by the scientific community. However, expanding applications of this whole massive dataset beyond ocean waves requires a strategy to automatically identify these geophysical phenomena. In this study, we propose to apply the emerging deep learning approach in ocean SAR scenes classification. The training is performed using a hand-curated dataset that describes ten commonly-occurring atmospheric or oceanic processes. Our model evaluation relies on an independent assessment dataset and shows satisfactory and robust classification results. To further illustrate the model performance, regional patterns of rain and sea ice are qualitatively analyzed and found to be very consistent with independent remote sensing datasets. In addition, these high-resolution WV SAR data can resolve fine, sub-km scale, spatial structure of rain events and sea ice that complement other satellite measurements. Overall, such automated SAR vignettes classification may open paths for broader geophysical application of maritime Sentinel-1 acquisitions.

A Novel Approach to Doppler Centroid and Channel Errors Estimation in Azimuth Multi-Channel SAR

Multi-channel synthetic aperture radar (SAR) in azimuth can overcome the minimum-antenna-area constraint of the conventional SAR in high-resolution and wide-swath (HRWS) imaging. However, the SAR system suffers from amplitude and phase mismatch among channels and nonideal antenna pattern, which will result in azimuth ambiguity and ghost targets in the final image. Therefore, taking the nonbandlimited signal and channel errors into account, a practical azimuth ambiguity-to-signal ratio (AASR) model of multi-channel SAR system is established. Meanwhile, the baseband Doppler centroid (DC) frequency related to channel errors also has an influence on image quality. Then, an effective method is proposed to calculate the baseband DC frequency according to the jumping points of the channel phase errors estimate. Subsequently, considering the effect of azimuth antenna pattern (AAP), a corresponding relationship between the ideal steering vectors and the signal subspace from the decomposing covariance matrix is established. After that, based on the uniqueness of the signal subspace and the correct corresponding relationship, an accurate method is proposed to estimate the channel phase errors by minimizing the minimum mean square error (MMSE) of the signal subspace. Finally, an accurate multi-channel SAR imaging diagram is shown to effectively mitigate the azimuth ambiguous energy caused by channel errors. Simulation and real data experiments, including four channel airborne SAR data with a bandwidth of 210 MHz and the Chinese Gaofen-3 dual receiving channel (DRC) spaceborne SAR data, validate the effectiveness of the proposed calibration method, particularly in low signal-to-noise ratio (SNR).

Refocusing of Moving Targets Based on Low-Bit Quantized SAR Data via Parametric Quantized Iterative Hard Thresholding

Low-bit quantization of echo improves storage and leads to more efficient downlink transmission of spaceborne synthetic aperture radar (SAR) systems. In this paper, a new parametric quantized iterative hard thresholding (PQIHT) algorithm is proposed to refocus the images of moving targets with low-bit quantized SAR data, based on the combination of quantized iterative hard thresholding (QIHT) and the parametric sparse representation. The blurred and quantization-error-involved subimage of the region of interest (ROI) containing the moving target is represented in a sparse fashion through an adaptive parametric dictionary. The QIHT with a pruned searching method is performed for efficiently estimating the motion-adaptive parameter inside the dictionary, refocusing the ROI image and suppressing the quantization-induced error in an iterative way. Different from the conventional QIHT algorithm with a fixed dictionary that can only represent stationary targets, the proposed method exploits a parametric dictionary with a parameter related to target motion status, which is capable of adaptively representing the radar echo from a moving target with unknown motion status and, therefore, is suitable for moving target refocusing. Simulations and experiments on real GF-3 satellite SAR data demonstrate that, compared with the conventional parametric sparse representation framework for moving target refocusing based on purely precise data, the proposed algorithm can provide satisfactory quality of moving target refocusing with remarkably reduced data volume.

Detecting the slope movement after the 2018 Baige Landslides based on ground-based and space-borne radar observations

On Oct. 11 and Nov. 3, 2018, two large-scale landslides occurred in the same location in Baige Village, Tibet, and massive rocks fell and encroached into the Jingsha River. These landslides posed a severe risk to the upstream and downstream areas. The occurrence, development and evolution of landslides are accompanied by a large number of changes in measurable variables. The deformation data are one of most important parameters for characterizing change and development trends of a landslide. This paper is centered on the results derived from ground-based radar and space-borne Synthetic Aperture Radar (SAR) images in the post-event phase to monitor the Baige landslides and to assess their residual risk. Two technologies play important roles in identifying and characterizing impending catastrophic slope failures: ground-based radar reveals the horizontal deformation, and satellite SAR images reveal the azimuth and range offset deformation. By combining satellite and ground-based SAR observations, we obtained high-precision three-dimensional (3D) deformation results and found that the vast majority of the instability regions mainly occur in the source area of the slope failures and that the direction of collapse converges from all sides to the middle. Additional information from UAV orthophoto maps and GNSS measurements also reveal that several cracks are distributed on the trailing edge of the landslide and are still moving. The comprehensive results revealed that the moving rock mass has still been remarkably active after the two landslide events. This study combined ground-based and space-borne SAR data to develop a long-term monitoring and stability evaluation process for implementation after a large landslide disaster. Based on the distribution characteristics of the 3D deformation fields, the present and future stability of the Baige Landslide was analyzed.

UTILIZATION OF SPACEBORNE C-BAND SAR DATA FOR ANALYSIS OF FLOOD IMPACT ON AGRICULTURE AND ITS MANAGEMENT

Abstract. North India, since time immemorial remained the cradle of agricultural development. Every year during the monsoons, there is a high upsurge in the river Rapti. This leads to heavy flooding in the area and takes a toll on life and property besides submerging acres of precious agricultural land. Till now, such floods have been mapped using satellite data from optical sensors, but in monsoons due to cloud cover the task becomes difficult. Spaceborne SAR (Synthetic Aperture RADAR) data solves this problem by offering all-weather availability. This study aims to utilize the potential of C-band dual PolSAR data from Sentinel-1A for flood mapping and analysis of its impact on agriculture along with suggesting mitigation measures. Sentinel-1A data of January and September 2018 was taken and pre-processed. Thereafter, an interferogram was prepared and was phase filtered using modified Goldstein phase filter to remove phase distortions and a DEM (Digital Elevation Model) was prepared. The phase maps generated were analyzed for phase variations after removing the topographic phase and it was found that there was very less phase variation from urban and agricultural lands surrounding the river which clearly indicated heavy flooding and submergence of land in water. Using the DEM map generated, hydrological processing was done to delineate flow lines and calculation of catchment area at point of maximum submergence of agricultural land. This was followed by the generation of contours in the area and finally, the flood impact analysis was done by calculating the water volume in watershed during the flood.

Moving ships refocusing for spaceborne SAR based on Doppler parameters estimation

Moving ships imaging is an important issue in marine remote-sensing by spaceborne synthetic aperture radar (SAR). As the moving ships defocus generally in high-resolution spaceborne SAR images, the refocusing step is demanded for the following processing in classification and identification. A new refocusing method based on Doppler parameters search is proposed to enhance the performance of ship images. This method is applied directly on the single-look complex image data. Furthermore, the space-variant characteristic of Doppler parameters is taken into account within this method. Finally, the GF-3 satellite SAR data refocusing results of moving ships verify the effectiveness of the proposed method.

Calibration of the Copolarized Backscattering Measurements From Gaofen-3 Synthetic Aperture Radar Wave Mode Imagery

Accurate absolute radiometric calibration of spaceborne synthetic aperture radar (SAR) sensor is of great importance in quantitative oceanic monitoring. Traditionally, the calibration constant is determined by analyzing measurements of man-made calibrators deployed on land at a high expense. In this paper, a technique called numerical weather prediction (NWP)-based ocean calibration method for estimating the calibration constant on the basis of SAR observations over ocean is applied to the first Chinese C-band SAR satellite Gaofen-3 (GF-3). The ocean calibration is performed on GF-3 wave mode SAR images at VV and HH polarizations over a period of one year from September 2017 to August 2018. Verification against the independent scatterometer-derived winds shows that after NWP-based ocean recalibration, the accuracy of wind speed retrieval from GF-3 wave mode imagery could reach the wind speed estimation performance using the state-of-the-art of SAR data. These results indicate that GF-3 wind retrievals are promising for operational oceanic products and applications if the SAR data are appropriately calibrated by the proposed NWP-based ocean calibration approach.

Comparison of Compact and Fully Polarimetric SAR for Multitemporal Wetland Monitoring

Spaceborne Synthetic Aperture Radar (SAR) instruments are effective tools for monitoring and mapping wetlands. With the availability of SAR instruments providing various polarization options, the scope of this study is to evaluate the new compact polarization for wetland multitemporal change detection using simulated RADARSAT Constellation Mission (RCM) SAR data. A series of fully polarimetric (FP) SAR images were collected over a test site located in Ontario, Canada, and used to simulate RCM compact polarimetric (CP) data. The simulated data were evaluated for multitemporal change detection and the results were compared to those from FP SAR data. WorldView imagery and water level data were used for analysis and validation of the change detection results. The study shows potential for using the CP SAR for multitemporal change detection over three major wetland classes: shallow water, marsh, and swamp. Although FP SAR was slightly more effective in multitemporal change detection compared to CP SAR, the percentage of agreement between the change detection results of FP and CP SAR was always greater than 90% for all wetland classes. The highest overall percentage of agreement (98.4%) between the results of FP and CP SAR was observed over the shallow water, while the lowest (92.5%) was observed over swamp.

Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique

Traditional studies on mapping wet snow cover extent (SCE) often feature limitations, especially in vegetated and mountainous areas. The aim of this study is to propose a new total and wet SCE mapping strategy based on freely accessible spaceborne synthetic aperture radar (SAR) data. The approach is transferable on a global scale as well as for different land cover types (including densely vegetated forest and agricultural regions), and is based on the use of backscattering coefficient, interferometric SAR coherence, and polarimetric parameters. Furthermore, four topographical factors were included in the simple tuning of random forest-based land cover type-dependent classification strategy. Results showed the classification accuracy was above 0.75, with an F-measure higher than 0.70, in all five selected regions of interest located around globally distributed mountain ranges. Whilst excluding forest-type land cover classes, the accuracy and F-measure increases to 0.80 and 0.75. In cross-location model set, the accuracy can also be maintained at 0.80 with non-forest accuracy up to 0.85. It has been found that the elevation and polarimetric parameters are the most critical factors, and that the quality of land cover information would also affect the subsequent mapping reliability. In conclusion, through comprehensive validation using optical satellite and in-situ data, our land cover-dependent total SCE mapping approach has been confirmed to be robustly applicable, and the holistic SCE map for different months were eventually derived.

Local Azimuth Ambiguity-to-Signal Ratio Estimation Method Based on the Doppler Power Spectrum in SAR Images

In synthetic aperture radar (SAR) images, azimuth ambiguity is one of the important factors that affect image quality. Generally, the azimuth ambiguity-to-signal ratio (AASR) is a measure of the azimuth ambiguity of SAR images. For the low signal-to-noise ratio (SNR) ocean areas, it is difficult to accurately estimate the local AASR using traditional estimation algorithms. In order to solve this problem, a local AASR estimation method based on the Doppler power spectrum in SAR images is proposed in this paper by analyzing the composition of the local Doppler spectrum of SAR images. The method not only has higher estimation accuracy under low SNR, but also overcomes the limitations of traditional algorithms on SAR images when estimating AASR. The feasibility and accuracy of the proposed method are verified by simulation experiments and spaceborne SAR data.

Characterization of rice agriculture in the Southern Province of Rwanda by means of microwave remote sensing

Wetlands can be considered as one of the most vulnerable and endangered ecosystems on the planet. The threat is even more emphasized when the “kidneys of the landscapes” are used for agricultural purposes. The agricultural intensification in Rwanda resulted year-round cultivation in most of the wetlands. Despite its higher water and labor demand than other crops paddy rice is the preferred and policy recommended food and cash crop in the wetlands of the country. For effective management planning processes to ensure food security there is a serious need for accurate and up-to-date information on the agriculture sector. The objective our study is to contribute to this effort by using spaceborne Synthetic Aperture Radar (SAR) data to retrieve phenological parameters of paddy rice in the Southern Province of Rwanda. High spatial resolution dual polarized Sentinel-1 SAR data were obtained for the time period covering Season B of the two rice cropping seasons of 2017. For the derivation of the selected phenological indicators (start, end, middle and length of season) time series analysis was performed. Based on the temporal pattern of radar backscatter values, the temporal and spatial distribution of SAR derived phenological indicators considerable differences were identified among the investigated four paddy rice fields. These differences are related to the different level of management harmonization, embedding other crops in the paddy rice fields, and as well as to the topographical characteristics of the valley bottoms where the cropping activity takes place. The study confirms that using SAR is a promising technology for agricultural monitoring especially in regions where cloud cover is the main reason for the limited use of optical imagery.

Sea Ice Classification Using TerraSAR-X ScanSAR Data With Removal of Scalloping and Interscan Banding

Effective methods of sea ice classification are crucial to regional ice type mapping using spaceborne synthetic aperture radar (SAR), especially in ScanSAR mode. However, in TerraSAR-X ScanSAR images of ocean scenes, the scalloping and interscan banding (ISB) artifacts are usually visible. Though these two artifacts could be reduced to a certain degree where residual artifacts do not impede the visual interpretation of SAR images, this is not the case when it comes to sea ice classification. The difficulty mainly comes from the involvement of texture features in sea ice classification. Texture features, especially gray level co-occurrence matrix (GLCM) are useful bases for sea ice classification using SAR data in single or dual polarization. When GLCM is applied to TerraSAR-X ScanSAR data, however, scalloping and ISB artifacts are found to be intensified, further affecting the classification result. How to largely eliminate the effects of scalloping and ISB on sea ice classification has not been studied thoroughly yet. In this paper, an approach combining Kalman filter, GLCM, and support vector machine is proposed. An independent testing shows that this approach is effective at the removal of scalloping and ISB's effects on sea ice classification using TerraSAR-X ScanSAR data, with the overall accuracy of 88.26%. Besides, convolutional neural network is implemented to compare with the proposed approach.

Automatic bathymetry retrieval from SAR images

Bathymetry, the topography of the sea floor, is in high demand due to the increase in offshore constructions like wind parks. It is also an important dataset for climate change modelling, when sea level rises and changes in circulation currents are to be simulated. The retrieval of accurate bathymetry data is a cost-intensive task usually requiring a survey vessel charting the respective area. However, bathymetry can also be retrieved remotely using data from Earth observation satellites. The main point of this study is the development of a processor that allows the automatic derivation of gridded bathymetry information from spaceborne Synthetic Aperture Radar (SAR) data. Observations of sea state modifications in SAR images are used to derive the bathymetry in shelf areas using the shoaling effect, which causes wavelengths to become shorter when reaching shallower waters. The water depth is derived using the dispersion relation for surface water waves, which requires wavelength and wave period as input parameters. While the wavelength can be directly retrieved from the SAR image, for the peak period additional information and procedures are required, e.g. local measurements or complex SAR data. A method for automatically deriving the wave period for swell waves in SAR images was developed and tested in this paper. It uses depth data from public databases as initial values which are compared to derived depths iterating through possible peak periods along the calculation grid; the peak period resulting in a minimal root-mean-square deviation is then used for bathymetry calculation. The bathymetry derived from a TerraSAR-X acquisition of the Channel Islands is presented; the resulting peak wave period of 11.3 s fits well to nearby in situ measurement data.

Retrieval of Ionospheric Faraday Rotation Angle in Low-Frequency Polarimetric SAR Data

A low-frequency spaceborne synthetic aperture radar (SAR) working system, e.g., operating at the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> -band or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> -band, has great advantages of military target detection and biomass monitoring. Nevertheless, it is more susceptible to ionospheric effects compared with the higher frequency system. A trans-ionospheric wave propagation model is established in this paper to incorporate ionospheric effects on SAR signals. As one of the significant distortion sources for the polarimetric SAR (PolSAR), Faraday rotation (FR) is mainly imposed by background ionosphere, and its spatial variation is discussed. FR estimators have been devised in succession to estimate FR angle (FRA), and various potential novel estimators can still be derived. But, from a viewpoint of theoretical expressions, the earliest estimator is bound to be the optimal one. Based on PolSAR real data, this mathematical conclusion is further validated via comprehensive performance analysis as to estimation bias and standard deviation rather than the existent root-mean-square principle. Finally, a step-by-step procedure of the FRA map is proposed and operated with an application of the airborne P-band PolSAR data. In particular, the ambiguity error of FRA estimates within a SAR observation is simulated and resolved. By processing the ALOS-2 real data, the spatial distribution of FRAs is retrieved and used to operate ionospheric total electron content soundings.

Capabilities of Chinese Gaofen-3 Synthetic Aperture Radar in Selected Topics for Coastal and Ocean Observations

Gaofen-3 (GF-3), the first Chinese spaceborne synthetic aperture radar (SAR) in C-band for civil applications, was launched on August 2016. Some studies have examined the use of GF-3 SAR data for ocean and coastal observations, but these studies generally focus on one particular application. As GF-3 has been in operation over two years, it is essential to evaluate its performance in ocean observation, a primary goal of the GF-3 launch. In this paper, we offer an overview demonstrating the capabilities of GF-3 SAR in ocean and coastal observations by presenting several representative cases, i.e., the monitoring of intertidal flats, offshore tidal turbulent wakes and oceanic internal waves, to highlight the GF-3’s full polarimetry, high spatial resolution and wide-swath imaging advantages. Moreover, we also present a detailed analysis of the use of GF-3 quad-polarization data for sea surface wind retrievals and wave mode data for sea surface wave retrievals. The case studies and statistical analysis suggest that GF-3 has good ocean and coastal monitoring capabilities, though further improvements are possible, particularly in radiometric calibration and stable image quality.